Difference between revisions of "Computational Thinking"

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Computational Practices (from http://cases.umd.edu/)
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We choose to deploy constructs from Computational Thinking (CT) in the context of linked data projects specifically with a mind toward teaching students how to think about the scaling problems when attempting to maximally organize sets of resources. We have two active projects in the [https://wikibase.slis.ua.edu/wiki/Main_Page Linked Data Research Group], one of which is well developed and will serve as our primary example to students and the second project is at a more nascent stage. They are:
  
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# <b>Data Practices</b>: The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies.
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# Organizing access to multimedia (still images and video clips) that document game action in college football games using data sources internal to a football game (e.g., statistical play-by-play datasets) and those data sources that are external (e.g., transcripts of television broadcasts).  
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## Collecting Data: Data are collected through observation and measurement. Computational tools play a key role in gathering and recording a variety of data across many different archival endeavors. Computational tools can be useful in different phases of data collection, including the design of the collection protocol, recording, and storage.
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# Organizing access to textual materials of the HathiTrust page scans by examining back-of-book indexes and ToCs as access mechanisms for internal book content. Also capturing features of interest to descriptive bibliographers.
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## Creating Data: The increasingly computational nature of working with archival data underscores the importance of developing computational thinking practices in the classroom. Part of the challenge is teaching students that answers are drawn from the data available. In many cases archivists use computational tools to generate data… at scales that would otherwise be impossible.
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## Manipulating Data: Computational tools make it possible to efficiently and reliably manipulate large and complex archival holdings. Data manipulation includes sorting, filtering, cleaning, normalizing, and joining disparate datasets.
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The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
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## Analyzing Data: There are many strategies that can be employed when analyzing data for use in an archival context, including looking for patterns or anomalies, defining rules to categorize data, and identifying trends and correlations.
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## Visualizing Data: Communicating results is an essential component of understanding archival data and computational tools can greatly facilitate that process. Tools include both conventional visualizations such as graphs and charts, as well as dynamic, interactive displays.
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We have chosen to distill our research and teaching efforts for using linked data to organize resource collections into the following two themes:
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# <b>Modeling and Simulation Practices</b>: The ability to create, refine, and use models of archival phenomena is a central practice… Models can include flowcharts and diagrams.
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<blockquote>
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## Using Computational Models to Understand a Concept: Computational models that demonstrate specific ideas or phenomena can serve as powerful learning tools. Students can use computational models to deepen their understanding of archival science.
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# Data-driven semantic indexing over RDF graphs
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## Using Computational Models to Find and Test a Solution: Computational models can be used to test hypotheses and discover solutions to problems. They make it possible to test many different solutions quickly, easily, and inexpensively before committing to a specific approach.
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# Our data is metadata
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## Assessing Computational Models: Students who have mastered this practice will be able to articulate the similarities and differences between a computational model and the phenomenon that it is modeling.
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</blockquote>
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## Designing Computational Models: Part of taking advantage of computational power… is designing new models that can be run on a computational device. Students… will be able to define the components of the model, describe how they interact, decide what data will be produced by the model.
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## Constructing Computational Models: An important practice… is the ability to create new or extend existing computational models. This requires being able to encode the model features in a way that a computer can interpret.
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Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.
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# <b>Computational Problem Solving Practices</b>: Problem solving is central to archival inquiry.
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## Preparing problems for Comp solutions
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==High-level Description of Project==
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## Programming
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## Choosing Effective Computational tools
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(For additional detailed information and background on this project, see [[Chronology: Data-driven Sports Image Indexing Research]] and sections of my [[2018-19 Academic Year Research Report]])<br>
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## Assessing different approaches to problem-solving
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<br>
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## Developing Modular Computational Solutions
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How can we index individual images that document football game action at scale? There are so many teams, so many photographers, so many photographs, all problems compounded by two material states of images (born digital and film photographs). Content-based computational methods are available to analyze images; however, these approaches alone are subject to [https://en.wikipedia.org/wiki/Semantic_gap#Image_analysis semantic gap] problems.
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## Creating Computational Abstractions
 
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## Troubleshooting and Debugging
 

Latest revision as of 17:01, 16 January 2021

We choose to deploy constructs from Computational Thinking (CT) in the context of linked data projects specifically with a mind toward teaching students how to think about the scaling problems when attempting to maximally organize sets of resources. We have two active projects in the Linked Data Research Group, one of which is well developed and will serve as our primary example to students and the second project is at a more nascent stage. They are:

  1. Organizing access to multimedia (still images and video clips) that document game action in college football games using data sources internal to a football game (e.g., statistical play-by-play datasets) and those data sources that are external (e.g., transcripts of television broadcasts).
  2. Organizing access to textual materials of the HathiTrust page scans by examining back-of-book indexes and ToCs as access mechanisms for internal book content. Also capturing features of interest to descriptive bibliographers.

The CT constructs below were adapted from Computational Practices Adapted to Archival Science work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.

We have chosen to distill our research and teaching efforts for using linked data to organize resource collections into the following two themes:

  1. Data-driven semantic indexing over RDF graphs
  2. Our data is metadata

Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.

High-level Description of Project

(For additional detailed information and background on this project, see Chronology: Data-driven Sports Image Indexing Research and sections of my 2018-19 Academic Year Research Report)

How can we index individual images that document football game action at scale? There are so many teams, so many photographers, so many photographs, all problems compounded by two material states of images (born digital and film photographs). Content-based computational methods are available to analyze images; however, these approaches alone are subject to semantic gap problems.

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